考虑转包的平行机供应链排序

研究一类考虑转包的供应链排序问题, 即工厂从客户处接受一批订单, 这些订单既可以由工厂完成, 也可以通过支付一定费用进行转包. 工厂需要确定被转包的订单集并安排未被转包订单的生产和运输. 针对工厂为平行机生产环境的情况, 以交货期限内完成所有订单的转包成本、生产成本与运输成本之和最小化为目标, 构建了问题的数学模型, 并设计了启发式算法. 最后通过数值实验结果表明了算法的有效性.

     

基于混沌蚁群算法的应急救援车辆调度优化

针对应急救援车辆调度优化问题的特征和需求,以可变双向距离、道路风险和成本最小为主要目标,建立了应急救援车辆调度优化问题的多目标优化模型.为避免过早陷入局部最优,提出了基于混沌扰动的改进蚁群系统优化算法.该算法可对信息素进行全局更新混沌扰动,有效地提高了算法的适应性、求解效率和求解质量.仿真实验表明该算法是可行的,能较好地满足应急救援车辆调度的优化需求.     

基于分区的航电系统调度分析工具实现

针对仅含纯周期任务集合、符合ARINC653多分区构架航电系统两级调度模型的可调度性判定问题,提出一种基于分区的航电系统调度分析工具。通过设定时钟变量模拟航电系统各分区中任务集调度过程,依据纯周期任务集及分区航电系统时间片分派特性确定仿真区间,设计优化的调度分析算法,判定航电系统分区级时间片分派的正确性及各分区中任务集的可调度性。测试及实例分析结果表明,该工具能自动、准确、快速地判定航电系统分区以及任务级调度模型的可调度性,并能以甘特图的方式绘制系统调度过程,较现有工具更为直观、高效。     

数据采集系统在中间试验工厂的开发与应用

基于中间试验工厂设备多而杂的特点,为了提升中间试验工厂的管理能力和设备利用率,对中间试验工厂的设备功能进行了研究,提出了基于Framework的分布式系统方案。在方案实施中,对与Framework系统不兼容的设备进行了改造升级,成功实现了各种不同接口设备生产数据的采集。并在此基础上开发出了专用的数据采集系统,为中间试验工厂的设备运行、科研调度提供了有力的技术支撑,同时也为其他钢厂设备的引进和数据采集系统的建立提供了可以借鉴的经验。     

The locomotive assignment problem: a survey on optimization models

The objective of the locomotive assignment problem (LAP) is to assign a fleet of locomotives to a set of trains while satisfying a rich set of operational and budget constraints and optimizing one or more crucial objectives. This paper presents a survey on optimization models for this class of scheduling problems. Depending on the context of application, the kind of model used to solve the LAP varies; accordingly, this influences the solution methodology and algorithms used. In this paper, we review and also suggest a classification of the various models.     

A bi‐objective approach to reschedule new jobs in a one machine model

We consider a one machine scheduling model, minimizing a classical objective function—either the total completion time or the maximum tardiness—and with two sets of jobs: one with initial jobs already scheduled and one with new jobs that must be inserted in the schedule. As such rescheduling can create a modification of the schedule of the initial jobs, a disruption objective is considered in addition to the original objective. This additional objective can be formulated in four different ways. Such model has been introduced by Hall and Potts, minimizing either a linear aggregation of the two objectives or the initial objective under a constraint giving an upper limit of the disruption objective. In this paper, the aim is to obtain the set of efficient schedules in regard to the two objectives. Algorithms are provided for the eight possible bi‐objective problems and illustrated by some didactic examples.     

A multiobjective hybrid ant colony optimization approach applied to the assignment and scheduling problem

The assignment and scheduling problem is inherently multiobjective. It generally involves multiple conflicting objectives and large and highly complex search spaces. The problem allows the determination of an efficient allocation of a set of limited and shared resources to perform tasks, and an efficient arrangement scheme of a set of tasks over time, while fulfilling spatiotemporal constraints. The main objective is to minimize the project makespan as well as the total cost. Finding a good approximation set is the result of trade‐offs between diversity of solutions and convergence toward the Pareto‐optimal front. It is difficult to achieve such a balance with NP‐hard problems. In this respect, and in order to efficiently explore the search space, a hybrid bidirectional ant‐based approach is proposed in this paper, which is an improvement of a bi‐colony ant‐based approach. Its main characteristic is that it combines a solution construction developed for a more complicated problem with a Pareto‐guided local search engine.     

无线传感器网络中的改进数据聚集调度算法

针对现有聚集数据调度近似算法具有较高延时上界的问题,提出一种改进的聚集数据调度近似算法。建立一棵根在中心结点的广度优先搜索树,分层构造一个最大独立集(MIS),使MIS中相邻的2个结点相距两跳。将MIS中的结点连接起来,形成一棵根在中心结点的数据聚集调度树,使结点按数据聚集调度树进行分层数据调度。在数据聚集调度树的构造过程中,对于任意支配点,以最小的结点连接其相距两跳的支配点。对于2个相邻支配点的公共邻居支配点,通过在距中心点最近的支配点加入数据聚集树,使其在数据调度过程中将数据发送给距中心点最近的支配点,从而降低数据的聚集延时。实验结果表明,与SAS算法、Guo’s算法和IAS算法相比,该算法的数据聚集延时更低,其延时上界为14R+△?10。     

一种基于区分服务的嵌套队列调度算法

现有队列调度算法只能满足某一种特定类型业务流量的服务质量(QoS)需求,无法广泛支持多种类型业务流量。为此,提出一种基于区分服务的嵌套队列调度算法。将现有队列调度算法通过嵌套模型进行组合,并根据该嵌套模型进行队列调度,从而为多种类型业务流量提供均衡的QoS保障。仿真结果表明,该算法能够满足不同类型业务流量的QoS需求,其各项性能指标所达效果与最优效果的差距较小,并且在多种类型业务流量的支持方面比现有队列调度算法表现更好。     

面向云存储的高性能数据隐私保护方法

随着云计算和云存储技术的飞速发展,越来越多的企业和个人使用云存储来保存数据或备份数据。但用户将私有数据上传到云端的同时,也丧失了对数据的绝对控制权,用户数据的隐私保护问题成为云存储发展不得不解决的问题。为了解决这一问题,提出一种新的针对云存储的数据隐私保护方法 BSBC(Bit Split Bit Combine),它在上传前,将数据按照比特位进行拆分,重新组装后形成多个数据文件,再分别上传到云存储服务器;下载时,先将所有数据文件下载,然后通过位合并再恢复成原始文件。实验证明这种方法可以保护用户数据的隐私,同时可比传统加解密获得17~35倍的性能提升。然后将核心的位拆分、位合并代码模块用汇编语言进行优化,对汇编语言进行指令调度优化,以减少数据冲突和流水线停顿。最终,采用BSBC方法比传统加解密可以获得25~35倍的性能提升。